Judged Useless, Queried Anyway: Tool-Using Agents Rarely Turn Their Own Evidence Judgments into Stopping Decisions

2026-10-05

AI Summary

This study reveals and validates a “decoupling of evidence judgment from behavioral decision-making” in AI agents: although agents can accurately identify invalid retrieval results, they rarely cease invoking the corresponding failed tools. Through controlled source-failure environments, comparative experiments, and preregistered replication studies, this work proposes a forced integration mechanism that directly constrains external actions using internal judgments without incurring additional reasoning overhead. Experimental results demonstrate that this mechanism significantly improves success rates across diverse models, rendering stopping decisions stable and robust to variations in computational budgets. Overall, this research establishes an efficient new paradigm for optimizing tool-calling behavior in AI agents by tightly coupling their evaluative assessments with actionable outputs.

Abstract

An agent whose tool keeps returning nothing useful should stop relying on it. In a retrieval environment with controlled source failures, we separate how agents judge results from what they do. We compare stopping at the same step after longer and shorter runs of results the agent judged useless; this contrast is zero for clock- or deadline-driven stopping. Where we record their judgments, the seven agents we test call a failing source's results useless 97-100% of the time, yet most of them rarely stop on that judgment. Prompt cues change when they stop but not what they stop on. Permission to answer from memory and a reasoning mode can bring early stops regardless of evidence, a stated budget moves the 7-8B models'stops to the deadline, and a stopping rule or call cost in the prompt is followed at most partly. Stopping follows the evidence only when the harness enforces an integration step that makes the agent answer after five consecutive results it judged useless. This step raises failing-source success for every model, keeps the stopping point fixed when the budget doubles, and needs no extra judgment call when the agent states its judgments. A pre-registered replication on 300 fresh questions confirms the dissociation and the rule's effect.

Problem

• Tool-Using Agents
• Stopping Decisions
• Evidence Judgments
• Retrieval

Innovation

• Tool-Using Agents
• Stopping Decisions
• Evidence Judgment
• Retrieval Augmented Generation
• Agent Harness